An InDepth Analysis of the ChungLu Model
In the classic Erd}os R enyi random graph model [5] each edge is chosen with uniform probability and the degree distribution is binomial, limiting the number of graphs that can be modeled using the Erd}os R enyi framework [10]. The ChungLu model [1, 2, 3] is an extension of the Erd}os R enyi model that allows for more general degree distributions. The probability of each edge is no longer uniform and is a function of a usersupplied degree sequence, which by design is the expected degree sequence of the model. This property makes it an easy model to work with theoretically and since the ChungLu model is a special case of a random graph model with a given degree sequence, many of its properties are well known and have been studied extensively [2, 3, 13, 8, 9]. It is also an attractive null model for many realworld networks, particularly those with powerlaw degree distributions and it is sometimes used as a benchmark for comparison with other graph generators despite some of its limitations [12, 11]. We know for example, that the average clustering coe cient is too low relative to most real world networks. As well, measures of a nitymore »
 Authors:

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 Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
 Publication Date:
 OSTI Identifier:
 1239211
 Report Number(s):
 LLNLTR678729
 DOE Contract Number:
 AC5207NA27344
 Resource Type:
 Technical Report
 Research Org:
 Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
 Sponsoring Org:
 USDOE
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING
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